Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

Tao, Jie; Liu, Yilun; Yang, Dalian

doi:10.1155/2016/9306205

Cited by 87 publications

(58 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DBN method can model nonlinear time series, so it was used to classify the different states of the bearing. Tao et al [83] employed a novel fault diagnosis approach utilizing multivibration signals and the DBN method. The current scheme could adaptively fuse multifeature data and identify various bearing faults by using the learning ability of DBN.…”

Section: Bearingsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

View full text Add to dashboard Cite

This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

show abstract

Section: Bearingsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

View full text Add to dashboard Cite

show abstract

“…Ma et al [235] applied DNN for bearing acceleration life test, which used the time-domain and frequency-domain features as raw inputs. Tao et al [236] proposed DBN for bearing fault diagnosis by using multi-sensor information, in which time-domain statistical features from three sensors served as the inputs. Chen et al [237] applied DBN-based DNN for gearbox fault diagnosis, in which a feature vector consisting of load and speed measure, time-domain, and frequency-domain features served as inputs.…”

Section: Deep Learningmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

View full text Add to dashboard Cite

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

show abstract

“…If these fatigue cracks cannot be timely detected and repaired, the subsequent fracture can bring catastrophic failure to the beam structures [7]. Recently, the vibration-based damage detection has become one of the commonly used tools for crack detection and diagnosis [8]. is approach is mainly based on changes in dynamic characteristics, such as natural frequency and mode shape [9].…”

Section: Introductionmentioning

confidence: 99%

Modelling a Cracked Beam Structure Using the Finite Element Displacement Method

Hui

Liu

Huang

et al. 2019

Shock and Vibration

Self Cite

View full text Add to dashboard Cite

A new model is presented for studying the effects of crack parameters on the dynamics of a cracked beam structure. The model is established by the finite element displacement method. In particular, the stiffness matrix of the cracked beam element is firstly derived by the displacement method, which does not need the flexibility matrix inversion calculation compared with the previous local flexibility approaches based on the force method. Starting with a finite element model of cracked beam element, the equation of strain energy of a cracked beam element is formed by the displacement method combined with the linear fracture mechanics. Then, based on the finite element method, the dynamic model of the cracked beam structure is obtained. The results show that the dynamic model discovers the internal relation between the dynamic characteristics of cracked beam structure and structural parameters, material parameters, and crack parameters. Finally, an example is presented to validate the proposed dynamic model.

show abstract

Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

Cited by 87 publications

References 24 publications

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Basic research on machinery fault diagnostics: Past, present, and future trends

Modelling a Cracked Beam Structure Using the Finite Element Displacement Method

Contact Info

Product

Resources

About